In order to make good match for a target moving model and its actual track,time series-based adaptive modeling in particle filter(TS_PF) is presented in this paper.The prediction model is dynamically made by the time series analysis.The states of weighted particles in particle filter are transferred according to the prediction model.By the resample technique of particle filter,the prediction error is further reduced and the prediction accuracy approximates to the optimal estimation.The simulations show that the time series adaptive modeling in particle filter can make good match with its actual track and overcome the defects of a single model′ s inaccuracy and IMM ′s apriorism.The accuracy of the dynamic target tracking is improved by TS_PF.